Mercurial > repos > bgruening > chemical_data_sources
view jmoleditor/jmoleditor.py @ 2:b65518a007fa draft
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author | bgruening |
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date | Sun, 08 Sep 2013 09:42:57 -0400 |
parents | 17a3f755d472 |
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#!/usr/bin/env python # Retrieves data from external data source applications and stores in a dataset file. # Data source application parameters are temporarily stored in the dataset file. import socket, urllib, sys, os from galaxy import eggs #eggs needs to be imported so that galaxy.util can find docutils egg... from galaxy.util.json import from_json_string, to_json_string import galaxy.model # need to import model before sniff to resolve a circular import dependency from galaxy.datatypes import sniff from galaxy.datatypes.registry import Registry from galaxy.jobs import TOOL_PROVIDED_JOB_METADATA_FILE assert sys.version_info[:2] >= ( 2, 4 ) def stop_err( msg ): sys.stderr.write( msg ) sys.exit() GALAXY_PARAM_PREFIX = 'GALAXY' GALAXY_ROOT_DIR = os.path.realpath( os.path.join( os.path.split( os.path.realpath( __file__ ) )[0], '..', '..' ) ) GALAXY_DATATYPES_CONF_FILE = os.path.join( GALAXY_ROOT_DIR, 'datatypes_conf.xml' ) def load_input_parameters( filename, erase_file = True ): datasource_params = {} try: json_params = from_json_string( open( filename, 'r' ).read() ) datasource_params = json_params.get( 'param_dict' ) except: json_params = None for line in open( filename, 'r' ): try: line = line.strip() fields = line.split( '\t' ) datasource_params[ fields[0] ] = fields[1] except: continue if erase_file: open( filename, 'w' ).close() #open file for writing, then close, removes params from file return json_params, datasource_params def __main__(): filename = sys.argv[1] try: max_file_size = int( sys.argv[2] ) except: max_file_size = 0 job_params, params = load_input_parameters( filename ) if job_params is None: #using an older tabular file enhanced_handling = False job_params = dict( param_dict = params ) job_params[ 'output_data' ] = [ dict( out_data_name = 'output', ext = 'data', file_name = filename, extra_files_path = None ) ] job_params[ 'job_config' ] = dict( GALAXY_ROOT_DIR=GALAXY_ROOT_DIR, GALAXY_DATATYPES_CONF_FILE=GALAXY_DATATYPES_CONF_FILE, TOOL_PROVIDED_JOB_METADATA_FILE = TOOL_PROVIDED_JOB_METADATA_FILE ) else: enhanced_handling = True json_file = open( job_params[ 'job_config' ][ 'TOOL_PROVIDED_JOB_METADATA_FILE' ], 'w' ) #specially named file for output junk to pass onto set metadata datatypes_registry = Registry() datatypes_registry.load_datatypes( root_dir = job_params[ 'job_config' ][ 'GALAXY_ROOT_DIR' ], config = job_params[ 'job_config' ][ 'GALAXY_DATATYPES_CONF_FILE' ] ) URL = params.get( 'URL', None ) #using exactly URL indicates that only one dataset is being downloaded URL_method = params.get( 'URL_method', None ) simpleD = params.get('galaxyData') # The Python support for fetching resources from the web is layered. urllib uses the httplib # library, which in turn uses the socket library. As of Python 2.3 you can specify how long # a socket should wait for a response before timing out. By default the socket module has no # timeout and can hang. Currently, the socket timeout is not exposed at the httplib or urllib2 # levels. However, you can set the default timeout ( in seconds ) globally for all sockets by # doing the following. socket.setdefaulttimeout( 600 ) cur_filename = params.get('output') outputfile = open( cur_filename, 'w' ).write( simpleD ) if __name__ == "__main__": __main__()